• 한국자동차공학회논문집
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• 제7권6호
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• pp.182-197
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• 1999

Described in this paper is an optimal driving control algorithm which focused on the improvement of fuel economy and the minimization of pollutant emissions in the parallel type hybrid drivertrain system for transit bus. For the energy balance among components such as engine, induction machine and buttery, the algorithm for power split ration determine is proposed. When it is implemented in the hybrid electric control unit(HECU) , using the sub-optimal method and the approximate technique , it is possible to save the memory , to shorten the calculation time, and to achieve the efficient driving actually. A Shift strategy for automated manual transmission is the other side of the driving control algorithm. It enables to select the optimal gear by using several shift maps which were predefined from the proposed method in this paper, As a results of driving simulation, it is proved that these algorithms make the hybrid drivetrain system to reduce fuel consumption and emissions considerably and to have the ability to the efficient use of battery.

• 한국안전학회지
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• 제21권5호
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• pp.103-111
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• 2006

The purpose of this study was to analyse the relationships between cognitive abilities and driving characteristics of elderly drivers. Driving characteristics of elderly and younger drivers who were driving a fixed base driving simulator vehicle were examined. Participants consisted of 12 drivers over age 65 (the 'older' group) and 12 drivers between the ages of 25 and 55 (the "younger" group). As indices of cognitive ability, critical flicker fusion frequency (CFF) tests and cognitive reaction tests were given before the driving task. CFF was also tested after the simulated driving task for both groups. Cognitive reaction tests, which were composed of speed estimation tests, multiple choice reaction tests and obstacle avoidance tests, were developed by the Korean Road Traffic Safety Authority in 2003. CFF values between the two groups exhibited significant differences both before and after the task, with a p-value less than 0.01 and a t-value of -3.01 before the test and a p-value less than 0.031 and a t-value of -2.35 after the test. Older drivers' CFF values were lower than those of the younger. However, there was no difference in older or younger driver CFF values before and after the task within the same group. Except for the multiple choice reaction test, there was no difference in cognitive reaction test results between the two groups. The elderly drivers made more errors though they did not differ from the younger drivers in reaction times. At the simulated driving task the reaction time of the elderly driver was longer than that of the younger; however, the driving speed of the elderly was lower and the number of collisions greater. There was a positive correlation (r=.496) between the number of errors in the multiple choice reaction test and the number of collisions in the driving task. Therefore, it was identified that critical attributes contributing to automobile crashes involving elderly drivers included cognitive difficulty in judging and responding to complex situations.

• 한국소음진동공학회논문집
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• 제14권1호
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• pp.17-23
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• 2004

The semi-active suspension system is getting widely adopted in passenger vehicles for its ability to improve ride comfort over the passive suspension system while not degrading driving safety. A key to the success is to develop practical controllers that yield performance enhancement over the passive damper under various driving conditions. To this end, several control strategies have been studied and evaluated in this research in consideration of practical aspects such as nonlinearity and dynamics of the damper. From simulation results. it has been observed that, with the proposed control schemes, ride comfort can be significantly upgraded while suppressing degradation of driving safety.

• 대한임베디드공학회논문지
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• 제18권5호
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• pp.225-231
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• 2023

With the recent surge in the autonomous driving market, the significance of lane detection technology has escalated. Lane detection plays a pivotal role in autonomous driving systems by identifying lanes to ensure safe vehicle operation. Traditional lane detection models rely on engineers manually extracting lane features from predefined environments. However, real-world road conditions present diverse challenges, hampering the engineers' ability to extract adaptable lane features, resulting in limited performance. Consequently, recent research has focused on developing deep learning based lane detection models to extract lane features directly from data. In this paper, we classify lane detection models into four categories: cluster-based, curve-based, information propagation-based, and anchor-based methods. We conduct an extensive analysis of the strengths and weaknesses of each approach, evaluate the model's performance on an embedded board, and assess their practicality and effectiveness. Based on our findings, we propose future research directions and potential enhancements.

• 한국기계가공학회지
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• 제17권2호
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• pp.77-82
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• 2018

Track shoes improve the off-road driving ability of tanks. The strength of the track shoe body directly affects the driving ability of tanks, self-propelled artillery, and armored vehicles. In this study, the design technique for track shoe body was investigated. To select the optimal design of track shoe body, three track shoe body models were suggested and compared. Tensile strength was calculated using computer-aided engineering (CAE) analysis. Compressive tests were conducted using the original tank sprocket because sprocket compression is critical to the lifespan of the track shoe body. As a result, one track shoe body design was selected and the process of track shoe body design was described.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2000년도 추계학술대회 논문집
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• pp.86-92
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• 2000

The crew operation scheduling problem is divided into duty generation and duty allocation. The railway crew roster scheduling(duty allocation) is developed monthly for each crew's office, considering the car types and crew's driving ability. However, it is very difficult to make schedules which assign monthly working hours to each crew evenly. The purpose of this thesis is to minimize the differences between each crew's total duty times and make a rostering table. To minimize the differences among monthly working hours of each crews are grouped according to car type and crew's driving ability and a rostering table which could be shared by the group members is developed. Our approach is to apply the techniques of Constraint Programming in order to solve such problems efficiently. This algorithm is suitable to solve optimization problems which involve many complicated constraints. And including the concept of cost, we suggested the model which enable to make the rostering table with a minimum personnel.

• 한국군사과학기술학회지
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• 제10권2호
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• pp.12-20
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• 2007

The aiming ability is a key to improve the accuracy performance of the gun pointing system in the ground military vehicle. This paper describes the new detection method of chatter vibration using disturbance acceleration in the pointing structure. In order to analysis the vibration trends of the pointing system occurred while the vehicle driving, acceleration data obtained from vehicle was processed by using data processing algorithm with moving average and Hilbert transform. The specific mode constants of acceleration were obtained from various disturbances. Vehicle velocity, road condition and property of pointing structure were considered as factors which make the change of vibration trend in vehicle dynamics. Finally, back propagation neural networks have been applied to the pattern recognition of the classification of vibration signal in various driving conditions. Results of signal processing were compared with other condition result and analysed.

• 전자통신동향분석
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• 제38권6호
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• pp.31-40
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• 2023

Given recent accidents involving autonomous vehicles, driver monitoring technology related to the transition of control in autonomous vehicles is gaining prominence. Driver status monitoring systems recognize the driver's level of alertness and identify possible impairments in the driving ability owing to conditions including drowsiness and distraction. In autonomous vehicles, predictive factors for the transition to manual driving should also be included. During traditional human driving, monitoring the driver's status is relatively straightforward owing to the consistency of crucial cues, such as the driver's location, head orientation, gaze direction, and hand placement. However, monitoring becomes more challenging during autonomous driving because of the absence of direct manual control and the driver's engagement in other activities, which may obscure the accurate assessment of the driver's readiness to intervene. Hence, safety-ensuring technology must be balanced with user experience in autonomous driving. We explore relevant global and domestic regulations, the new car assessment program, and related standards to extract requirements for driver status monitoring. This kind of monitoring can both enhance the autonomous driving performance and contribute to the overall safety of autonomous vehicles on the road.

• 로봇학회논문지
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• 제10권1호
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• pp.33-41
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• 2015

Recent developments in robotics and intelligent vehicle area, bring interests of people in an autonomous driving ability and advanced driving assistance system. Especially fully automatic parking ability is one of the key issues of intelligent vehicles, and accurate parked vehicles detection is essential for this issue. In previous researches, many types of sensors are used for detecting vehicles, 2D LiDAR is popular since it offers accurate range information without preprocessing. The L shape feature is most popular 2D feature for vehicle detection, however it has an ambiguity on different objects such as building, bushes and this occurs misdetection problem. Therefore we propose the accurate vehicle detection method by using a 3D complete vehicle model in 3D point clouds acquired from front inclined 2D LiDAR. The proposed method is decomposed into two steps: vehicle candidate extraction, vehicle detection. By combination of L shape feature and point clouds segmentation, we extract the objects which are highly related to vehicles and apply 3D model to detect vehicles accurately. The method guarantees high detection performance and gives plentiful information for autonomous parking. To evaluate the method, we use various parking situation in complex urban scene data. Experimental results shows the qualitative and quantitative performance efficiently.

• 한국멀티미디어학회논문지
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• 제18권3호
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• pp.387-400
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• 2015

A fuzzy logic based mobile robot navigation system was developed to improve the driving ability without trapping inside obstacles in complex terrains, which is one of the most concerns in robot navigation in unknown terrains. The navigation system utilizes the data from ultrasonic sensors to recognize the distances from obstacles and the position information from a GPS sensor. The fuzzy navigation system has two groups of behavior rules, and the robot chooses one of them based on the information from sensors while navigating for the targets. In plain terrains the robot with the proposed algorithm uses one rule group consisting of behavior rules for avoiding obstacle, target steering, and following edge of obstacle. Once trap is detected the robot uses the other rule group consisting of behavior rules strengthened for following edge of obstacle. The output signals from navigation system control the speed of two wheels of the robot through the fuzzy logic data process. The test was conducted in the Matlab based mobile robot simulator developed in this study, and the results show that escaping ability from obstacle is improved.